Quantification of the relative risk of multiple occlusal variables for muscle disorders of the stomatognathic system

Abstract

Statement of problem There is no consensus on the association between occlusion and temporomandibular disorders (TMD). Purpose The purpose of this study was to quantify the relative risk of multiple occlusal variables for muscle disorders of the stomatognathic system. Material and methods Eight occlusal features: retruded contact position (RCP) to maximum intercuspation (MI) slide length, vertical overlap, horizontal overlap, unilateral posterior reverse articulation, anterior open occlusal relationship, incisor dental midline discrepancy, mediotrusive interferences, and laterotrusive interferences, were clinically assessed by the same trained operator. The sample consisted of 81 women with a Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) axis I diagnosis of muscle disorder, for example myofascial pain, with or without limited opening, and 48 healthy women (control group). A stepwise multiple logistic regression was used to identify the significant associations between occlusal features and disease. Results A slide from the retruded contact position to maximum intercuspation ≥2 mm and mediotrusive interferences were the only 2 occlusal features significantly associated with the presence of myofascial pain according to the RDC/TMD criterion symptoms. The odds ratio for myofascial pain was 2.57 for a slide from RCP to MI ≥2 mm and 2.45 for mediotrusive interferences. The percentage of the total log likelihood for myofascial pain explained by the significant occlusal factors amounted to 10.8% (Nagelkerke's R 2 = 0.108). The multifactorial model, including the 2 significant occlusal factors, showed an acuracy to predict disease of 66.7% (sensitivity 71.6%; specificity 58.3%). Conclusion Occlusal features showed a low predictive value to detect muscle disorders of the stomatognathic system. Multifactorial complex pathologies, such as TMD, should be studied using multivariate statistical analyses, as univariate models may overestimate some resulting associations.